
import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
							from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


lowerCamelCase_    :			int    =	logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase_    :			List[Any]    =	2_56

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Optional[int]          =			["""melgan"""]

			def __init__(							self			:							Union[str, Any]    ,						snake_case_			:							SpectrogramNotesEncoder    ,						snake_case_			:							SpectrogramContEncoder    ,						snake_case_			:							TaFilmDecoder    ,						snake_case_			:							DDPMScheduler    ,						snake_case_			:							OnnxRuntimeModel if is_onnx_available() else Any    ,						):
							super().__init__()

							# From MELGAN
							UpperCamelCase_:				Optional[Any]						=    math.log(1e-5   )  # Matches MelGAN training.
							UpperCamelCase_:				int						=    4.0  # Largest value for most examples
							UpperCamelCase_:				Any						=    128

							self.register_modules(
							    notes_encoder=snake_case_    ,						continuous_encoder=snake_case_    ,						decoder=snake_case_    ,						scheduler=snake_case_    ,						melgan=snake_case_    ,						)

			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							List[str]    ,						snake_case_			:							Optional[int]=(-1.0, 1.0)    ,						snake_case_			:							List[Any]=False   ):
							UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    output_range
							if clip:
											UpperCamelCase_:				Tuple						=    torch.clip(snake_case_    ,						self.min_value    ,						self.max_value   )
							# Scale to [0, 1].
							UpperCamelCase_:				Optional[int]						=    (features - self.min_value) / (self.max_value - self.min_value)
							# Scale to [min_out, max_out].
							return zero_one * (max_out - min_out) + min_out

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							List[Any]=(-1.0, 1.0)    ,						snake_case_			:							List[Any]=False   ):
							UpperCamelCase_, UpperCamelCase_:				Any						=    input_range
							UpperCamelCase_:				Any						=    torch.clip(snake_case_    ,						snake_case_    ,						snake_case_   ) if clip else outputs
							# Scale to [0, 1].
							UpperCamelCase_:				List[Any]						=    (outputs - min_out) / (max_out - min_out)
							# Scale to [self.min_value, self.max_value].
							return zero_one * (self.max_value - self.min_value) + self.min_value

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							int    ,						snake_case_			:							Union[str, Any]   ):
							UpperCamelCase_:				Tuple						=    input_tokens > 0
							UpperCamelCase_, UpperCamelCase_:				Dict						=    self.notes_encoder(
							    encoder_input_tokens=snake_case_    ,						encoder_inputs_mask=snake_case_   )

							UpperCamelCase_, UpperCamelCase_:				List[Any]						=    self.continuous_encoder(
							    encoder_inputs=snake_case_    ,						encoder_inputs_mask=snake_case_   )

							return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							Any    ,						snake_case_			:							Any    ,						snake_case_			:							Optional[int]   ):
							UpperCamelCase_:				str						=    noise_time
							if not torch.is_tensor(snake_case_   ):
											UpperCamelCase_:				Dict						=    torch.tensor([timesteps]    ,						dtype=torch.long    ,						device=input_tokens.device   )
							elif torch.is_tensor(snake_case_   ) and len(timesteps.shape   ) == 0:
											UpperCamelCase_:				Dict						=    timesteps[None].to(input_tokens.device   )

							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							UpperCamelCase_:				str						=    timesteps * torch.ones(input_tokens.shape[0]    ,						dtype=timesteps.dtype    ,						device=timesteps.device   )

							UpperCamelCase_:				Optional[Any]						=    self.decoder(
							    encodings_and_masks=snake_case_    ,						decoder_input_tokens=snake_case_    ,						decoder_noise_time=snake_case_   )
							return logits




			@torch.no_grad()
			def __call__(							self			:							List[Any]    ,						snake_case_			:							List[List[int]]    ,						snake_case_			:							Optional[torch.Generator] = None    ,						snake_case_			:							int = 100    ,						snake_case_			:							bool = True    ,						snake_case_			:							str = "numpy"    ,						snake_case_			:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,						snake_case_			:							int = 1    ,						):
							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(snake_case_    ,						snake_case_   ) or callback_steps <= 0)
							):
											raise ValueError(
											    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
											    f''' {type(snake_case_   )}.'''   )

							UpperCamelCase_:				Dict						=    np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]    ,						dtype=np.floataa   )
							UpperCamelCase_:				Tuple						=    np.zeros([1, 0, self.n_dims]    ,						np.floataa   )
							UpperCamelCase_:				List[Any]						=    torch.ones((1, TARGET_FEATURE_LENGTH)    ,						dtype=snake_case_    ,						device=self.device   )

							for i, encoder_input_tokens in enumerate(snake_case_   ):
											if i == 0:
															UpperCamelCase_:				Any						=    torch.from_numpy(pred_mel[:1].copy()   ).to(
															    device=self.device    ,						dtype=self.decoder.dtype   )
															# The first chunk has no previous context.
															UpperCamelCase_:				Dict						=    torch.zeros((1, TARGET_FEATURE_LENGTH)    ,						dtype=snake_case_    ,						device=self.device   )
											else:
															# The full song pipeline does not feed in a context feature, so the mask
															# will be all 0s after the feature converter. Because we know we're
															# feeding in a full context chunk from the previous prediction, set it
															# to all 1s.
															UpperCamelCase_:				Dict						=    ones

											UpperCamelCase_:				Optional[int]						=    self.scale_features(
											    snake_case_    ,						output_range=[-1.0, 1.0]    ,						clip=snake_case_   )

											UpperCamelCase_:				Dict						=    self.encode(
											    input_tokens=torch.IntTensor([encoder_input_tokens]   ).to(device=self.device   )    ,						continuous_inputs=snake_case_    ,						continuous_mask=snake_case_    ,						)

											# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
											UpperCamelCase_:				List[Any]						=    randn_tensor(
											    shape=encoder_continuous_inputs.shape    ,						generator=snake_case_    ,						device=self.device    ,						dtype=self.decoder.dtype    ,						)

											# set step values
											self.scheduler.set_timesteps(snake_case_   )

											# Denoising diffusion loop
											for j, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
															UpperCamelCase_:				Union[str, Any]						=    self.decode(
															    encodings_and_masks=snake_case_    ,						input_tokens=snake_case_    ,						noise_time=t / self.scheduler.config.num_train_timesteps    ,						)

															# Compute previous output: x_t -> x_t-1
															UpperCamelCase_:				List[Any]						=    self.scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Any						=    self.scale_to_features(snake_case_    ,						input_range=[-1.0, 1.0]   )
											UpperCamelCase_:				Any						=    mel[:1]
											UpperCamelCase_:				Dict						=    mel.cpu().float().numpy()

											UpperCamelCase_:				Tuple						=    np.concatenate([full_pred_mel, pred_mel[:1]]    ,						axis=1   )

											# call the callback, if provided
											if callback is not None and i % callback_steps == 0:
															callback(snake_case_    ,						snake_case_   )

											logger.info("""Generated segment"""    ,						snake_case_   )

							if output_type == "numpy" and not is_onnx_available():
											raise ValueError(
											    """Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."""   )
							elif output_type == "numpy" and self.melgan is None:
											raise ValueError(
											    """Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."""   )

							if output_type == "numpy":
											UpperCamelCase_:				List[str]						=    self.melgan(input_features=full_pred_mel.astype(np.floataa   )   )
							else:
											UpperCamelCase_:				Optional[int]						=    full_pred_mel

							if not return_dict:
											return (output,)

							return AudioPipelineOutput(audios=snake_case_   )








lowerCamelCase_    :			Tuple    =	{
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowerCamelCase_    :			Union[str, Any]    =	{
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						float:
				UpperCamelCase_:				Any						=    from_type.lower().strip("""s"""    )
				UpperCamelCase_:				int						=    to_type.lower().strip("""s"""    )

				UpperCamelCase_:				Any						=    UNIT_SYMBOL.get(lowerCamelCase  ,     lowerCamelCase    )
				UpperCamelCase_:				str						=    UNIT_SYMBOL.get(lowerCamelCase  ,     lowerCamelCase    )

				if from_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:				Optional[int]						=    (
								    F'''Invalid \'from_type\' value: {from_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase    )}'''
								)
								raise ValueError(lowerCamelCase    )
				if to_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:				Dict						=    (
								    F'''Invalid \'to_type\' value: {to_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase    )}'''
								)
								raise ValueError(lowerCamelCase    )
				UpperCamelCase_:				Union[str, Any]						=    METRIC_CONVERSION[from_sanitized]
				UpperCamelCase_:				str						=    METRIC_CONVERSION[to_sanitized]
				UpperCamelCase_:				Tuple						=    1

				if from_exponent > to_exponent:
								UpperCamelCase_:				Union[str, Any]						=    from_exponent - to_exponent
				else:
								UpperCamelCase_:				Dict						=    -(to_exponent - from_exponent)

				return value * pow(10  ,     lowerCamelCase    )


if __name__ == "__main__":
							from doctest import testmod

							testmod()






